

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath

class Block(nn.Module):
	r""" ConvNeXt Block. There are two equivalent implementations:
	(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
	(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
	We use (2) as we find it slightly faster in PyTorch
	
	Args:
		dim (int): Number of input channels.
		drop_path (float): Stochastic depth rate. Default: 0.0
		layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
	"""
	def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
		super().__init__()
		self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
		self.norm = LayerNorm(dim, eps=1e-6)
		self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
		self.act = nn.GELU()
		self.pwconv2 = nn.Linear(4 * dim, dim)
		self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), 
									requires_grad=True) if layer_scale_init_value > 0 else None
		self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

	def forward(self, x):
		input = x
		x = self.dwconv(x)
		x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
		x = self.norm(x)
		x = self.pwconv1(x)
		x = self.act(x)
		x = self.pwconv2(x)
		if self.gamma is not None:
			x = self.gamma * x
		x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)

		x = input + self.drop_path(x)
		return x

class ConvNeXt(nn.Module):
	r""" ConvNeXt
		A PyTorch impl of : `A ConvNet for the 2020s`  -
		  https://arxiv.org/pdf/2201.03545.pdf
	Args:
		in_chans (int): Number of input image channels. Default: 3
		num_classes (int): Number of classes for classification head. Default: 1000
		depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
		dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
		drop_path_rate (float): Stochastic depth rate. Default: 0.
		layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
		head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
	"""
	def __init__(self, in_chans=3, num_classes=1000, 
				 depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0., 
				 layer_scale_init_value=1e-6, head_init_scale=1.,
				 ):
		super().__init__()

		self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers
		stem = nn.Sequential(
			nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
			LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
		)
		self.downsample_layers.append(stem)
		for i in range(3):
			downsample_layer = nn.Sequential(
					LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
					nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
			)
			self.downsample_layers.append(downsample_layer)

		self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks
		dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] 
		cur = 0
		for i in range(4):
			stage = nn.Sequential(
				*[Block(dim=dims[i], drop_path=dp_rates[cur + j], 
				layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]
			)
			self.stages.append(stage)
			cur += depths[i]

		self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer
		self.head = nn.Linear(dims[-1], num_classes)

		self.apply(self._init_weights)
		self.head.weight.data.mul_(head_init_scale)
		self.head.bias.data.mul_(head_init_scale)

	def _init_weights(self, m):
		if isinstance(m, (nn.Conv2d, nn.Linear)):
			trunc_normal_(m.weight, std=.02)
			nn.init.constant_(m.bias, 0)

	def forward_features(self, x):
		for i in range(4):
			x = self.downsample_layers[i](x)
			x = self.stages[i](x)
		return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C)

	def forward(self, x):
		x = self.forward_features(x)
		x = self.head(x)
		return x

class LayerNorm(nn.Module):
	r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. 
	The ordering of the dimensions in the inputs. channels_last corresponds to inputs with 
	shape (batch_size, height, width, channels) while channels_first corresponds to inputs 
	with shape (batch_size, channels, height, width).
	"""
	def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
		super().__init__()
		self.weight = nn.Parameter(torch.ones(normalized_shape))
		self.bias = nn.Parameter(torch.zeros(normalized_shape))
		self.eps = eps
		self.data_format = data_format
		if self.data_format not in ["channels_last", "channels_first"]:
			raise NotImplementedError 
		self.normalized_shape = (normalized_shape, )
	
	def forward(self, x):
		if self.data_format == "channels_last":
			return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
		elif self.data_format == "channels_first":
			u = x.mean(1, keepdim=True)
			s = (x - u).pow(2).mean(1, keepdim=True)
			x = (x - u) / torch.sqrt(s + self.eps)
			x = self.weight[:, None, None] * x + self.bias[:, None, None]
			return x


def convnext_tiny(pretrained=False, **kwargs):
	model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
	if pretrained:
		checkpoint = torch.load('convnext/convnext_tiny_1k_224_ema.pth')
		model.load_state_dict(checkpoint["model"])
	return model

def convnext_small(pretrained=False, **kwargs):
	model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
	if pretrained:
		checkpoint = torch.load('convnext/convnext_small_1k_224_ema.pth')
		model.load_state_dict(checkpoint["model"])
	return model

def convnext_base(pretrained=False, in_22k=False, **kwargs):
	model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
	if pretrained:
		checkpoint = torch.load('convnext/convnext_base_1k_224_ema.pth')
		model.load_state_dict(checkpoint["model"])
	return model

def convnext_large(pretrained=False, in_22k=False, **kwargs):
	model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
	if pretrained:
		checkpoint = torch.load('convnext/convnext_large_1k_224_ema.pth')
		model.load_state_dict(checkpoint["model"])
	return model